Developer Proposes 20-Task Coding Harness to Replace Vibe-Based Model Comparisons

A developer writing on DEV Community argues that most informal comparisons of AI coding models amount to little more than subjective guesswork, since pasting prompts into two models and skimming results is not a rigorous evaluation. To address this, they propose a lightweight, repeatable testing harness consisting of just two files — a JSONL task list and a Python runner — covering 20 fixed coding tasks with deterministic and rubric-based scoring. The suite is designed to test failure modes specific to a developer's own stack, such as hallucinated imports, meaningless test assertions, or SQL edge-case errors, rather than relying on public benchmarks built around others' priorities. Running the harness multiple times across several candidate models can generate hundreds of API calls, making free-tier model access practically significant for iteration. The author disclosed the article was prepared as part of outreach for MonkeyCode, though the harness itself is compatible with any OpenAI-compatible API endpoint.